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Fast extraction of adaptive multiresolution meshes with guaranteed properties from volumetric data

Gavriliu, Marcel and Carranza, Joel and Breen, David E. and Barr, Alan H. (2001) Fast extraction of adaptive multiresolution meshes with guaranteed properties from volumetric data. In: VIS '01 Proceedings of the conference on Visualization '01. IEEE , Piscataway, NJ, pp. 295-303. ISBN 0-7803-7201-8. https://resolver.caltech.edu/CaltechAUTHORS:20161025-174556601

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Abstract

We present a new algorithm for extracting adaptive multiresolution triangle meshes from volume datasets. The algorithm guarantees that the topological genus of the generated mesh is the same as the genus of the surface embedded in the volume dataset at all levels of detail. In addition to this "hard constraint" on the genus of the mesh, the user can choose to specify some number of soft geometric constraints, such as triangle aspect ratio, minimum or maximum total number of vertices, minimum and/or maximum triangle edge lengths, maximum magnitude of various error metrics per triangle or vertex, including maximum curvature (area) error, maximum distance to the surface, and others. The mesh extraction process is fully automatic and does not require manual adjusting of parameters to produce the desired results as long as the user does not specify incompatible constraints. The algorithm robustly handles special topological cases, such as trimmed surfaces (intersections of the surface with the volume boundary), and manifolds with multiple disconnected components (several closed surfaces embedded in the same volume dataset). The meshes may self-intersect at coarse resolutions. However, the self-intersections are corrected automatically as the resolution of the meshes increase. We show several examples of meshes extracted from complex volume datasets.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1109/VISUAL.2001.964524DOIPaper
http://ieeexplore.ieee.org/document/964524/PublisherPaper
Additional Information:© 2001 IEEE. We would like to thank Mathieu Desbrun, Mark Ellisman, Gordon Kindlmann, Maryann Martone, Sean Mauch, Ken Museth, Cici Koenig, Ross Whitaker, Leonid Zhukov, and the Stanford University Computer Graphics Lab for their assistance and support. This work was supported by National Science Foundation grants #ASC-89-20219 and #ACI-9982273; the Office of the Director of Defense Research and Engineering, and the Air Force Office of Scientific Research (F49620-96-1-0471), as part of the MURI program; and the Caltech SURF Program.
Funders:
Funding AgencyGrant Number
NSFASC-89-20219
NSFACI-9982273
Office of the Director of Defense Research and EngineeringUNSPECIFIED
Air Force Office of Scientific Research (AFOSR)F49620-96-1-0471
Caltech Summer Undergraduate Research Fellowship (SURF)UNSPECIFIED
Record Number:CaltechAUTHORS:20161025-174556601
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20161025-174556601
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:71490
Collection:CaltechAUTHORS
Deposited By: Kristin Buxton
Deposited On:27 Oct 2016 16:49
Last Modified:03 Oct 2019 16:08

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